How to fit data!
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I have experimental data and I need to represent it as a function of x using the MATLAB fitting tool.
However, the obtained experimental data were non-linear and were fitted by dividing the intervals.
The problem here is that the fitting is made so that the function values at the points connecting the sections do not match.
Is there a way or code to fix and fit the end value of the interval?
A data example is attached.
It's a long question, but I'll wait for an answer. Thank you.
(If there is an efficient way to represent it as a function, please let me know!)
- There may be some awkward parts using a translator! :)
4 个评论
KSSV
2022-1-25
Did you try polyfit?
재열 김
2022-1-26
Image Analyst
2022-1-26
You said "A data example is attached." but there is no attachment. Use the paperclip icon to attach your file, and give us the code to read it in and plot it.
재열 김
2022-1-26
采纳的回答
Star Strider
2022-1-25
0 个投票
This is something similar to Curve Fitting of large Data Measurement? however with a different sort of envelope. I would begin by using the envelope function to get the general shape of the envelope, derive a function that describes it mathematically in terms of the other data (time, amplitude, frequency, perhaps others), then change the code in the Answer I linked to in order to fit the entire function to the data (unless the desired result is only to determine a function for the envelope, if so stop after the envelope determination step).
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재열 김
2022-1-26
Thanks for your reply!
I thought it was right to approach the data fitting like the page you linked to, so I modified the code to run it. But, as shown in the picture, I found that they are not very similar.
Should I use peak envelope (to get a function of x) to fit as closely as possible to the original data?
It ocurred to me that this could be a straighttforward system identification problem, and would automatically code for the envelope function as well, since that may not be possible to correctly estimate otherwise.
For best results, I need to know what the inputs were to the system that produced those outputs. It should be a vector (or matrix) of corresponding sample times. The system identification is not going to work well without knowing the inputs.
T1 = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/874645/h2_f_og_.csv', 'VariableNamingRule','preserve')
x = T1.x;
Fx = T1.Fx;
Ts = mean(diff(x)) % Sampling Interval
% Tsd = std(diff(x)) % Check Sampling Time Consistency
Fx_data = iddata(Fx,[],Ts)
Fxss_sys = ssest(Fx_data, 8)
figure
compare(Fx_data, Fxss_sys)
.
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